Field of the Invention
The disclosure relates generally to management systems and methods and, more particularly to distributed systems and methods for database management and management systems thereof.
Description of the Related Art
In recent years, with the development of technology and the Internet, there have been larger amount of computing that need to be processed, such as scientific computing, cloud services, and so on. This large-scale computing requires a large distributed management system for support. Due to increased network bandwidth, the geographical restrictions are broken such that different distributed systems from everywhere can be integrated. However, with the technological advanced, hardware purchased in different times will vary significantly, widening differences among the hardware equipment resources in the distributed systems and making computing resource management for the distributed system become complex. In addition, as the distributed system is no longer subject to geographical limitations, the network and performance have a more significant impact on the system, thus leading to increased resource variability and more difficulty involved in grasping the system machine status.
Currently, the data in the database of the distributed system is mostly stored in a hard disk. Due to the characteristics of the distributed system, data partition for storage in different locations is often needed. For databases managed by large systems, such as Google™, Yahoo™, and other portals, there is a large amount of data that need to be partitioned for storage in different hard disks at different servers. When specific data is needed, it has to be accessed from the hard disk of different servers, resulting in difficult data access.
In addition, current distributed system database management techniques, such as the dynamic data partition technique, mainly studies how research data can be moved among different hard disks. The database partition method is used to manage data in different database servers, which will result in inability to increase execution through a need to continuously re-partition data, leading to poor execution performance.